Quickstart

This is the shortest path from a built module to a result. It runs one of the bundled cases end to end and shows you what a successful run looks like. It does not explain the model: for that, read First run (write the smallest program yourself) or the Tutorial A->Z.

Before you start

You need the pops module built and importable. If you have not built it yet, follow Installation, then come back here. To confirm the module is on your path:

python -c "import pops; print(pops.__file__)"

The command prints the path to the compiled module. If it raises ModuleNotFoundError, set PYTHONPATH to the build directory that contains the .so (see Installation).

Run a bundled case

The named scenarios (diocotron, euler_poisson, …) live in the adc_cases repository, not in adc_cpp. Clone it next to the module and run a case:

git clone https://github.com/wolf75222/adc_cases.git
cd adc_cases
python diocotron/run.py

Most cases accept a reduced mode for a fast smoke run. Pass it when you only want to confirm the pipeline works:

python diocotron/run.py --quick

What success looks like

The run prints the simulation time and a conserved quantity at each report step, then writes its figures next to the case. For the diocotron case you get the density snapshots and the growth-rate plot. A run that ends without a Python traceback and produces those files has succeeded.

Next steps

• First run: write the smallest pops program yourself, one block, copyable as is.

• Tutorial A->Z: the full 18-step path, from git clone to the uniform versus AMR comparison.

• Repository layout: how adc_cpp (the library) and adc_cases (the scenarios) fit together.